Evaluating and Explaining Climate Science

Archive for December, 2013

In Part Seven we looked at some early GCM work – late 80’s to mid 90’s. In Part Eight we looked at some papers from the “Noughties” – atmospheric GCMs with prescribed ocean temperatures and some intermediate complexity models.

All of these papers were attempting to do the most fundamental of ice age inception – perennial snow cover at high latitudes. Perennial snow cover may lead to permanent ice sheets – but it may not. This requires an ice sheet model which handles the complexities of how ice sheets grow, collapse, slide and transfer heat.

Given the computational limitations of models even running a model to produce (or not) the basics of perennial snow cover has not been a trivial exercise, but a full atmospheric ocean GCM with an ice sheet model run for 130,000 years was not a possibility.

In this article we will look at a very recent paper, where fully coupled GCMs are used. “Fully coupled” means an atmospheric model and an ocean model working in tandem – transferring heat, moisture and momentum.

Smith & Gregory (2012)

The problem:

It is generally accepted that the timing of glacials is linked to variations in solar insolation that result from the Earth’s orbit around the sun (Hays et al. 1976; Huybers and Wunsch 2005). These solar radiative anomalies must have been amplified by feedback processes within the climate system, including changes in atmospheric greenhouse gas (GHG) concentrations (Archer et al. 2000) and ice-sheet growth (Clark et al. 1999), and whilst hypotheses abound as to the details of these feedbacks, none is without its detractors and we cannot yet claim to know how the Earth system produced the climate we see recorded in numerous proxy records. This is of more than purely intellectual interest: a full understanding of the carbon cycle during a glacial cycle, or the details of how regional sea-level changed as the ice-sheets waxed and waned would be of great use in accurately predicting the future climatic effects of anthropogenic CO2 emissions, as we might expect many of the same fundamental feedbacks to be at play in both scenarios..

..The multi-millennial timescales involved in modelling even a single glacial cycle present an enormous challenge to comprehensive Earth system models based on coupled atmosphere–ocean general circulation models (AOGCMs). Due to the computational expense involved, AOGCMs are usually limited to runs of a few hundred years at most, and their use in paleoclimate studies has generally been through short, ‘‘snapshot’’ runs of specific periods of interest.

Transient simulations of glacial cycles have hitherto only been run with models where important climate processes such as clouds or atmospheric moisture transports are more crudely parameterised than in an AOGCM or omitted entirely. The heavy restrictions on the feedbacks involved in such models limit what we can learn of the evolution of the climate from them, particularly in paleoclimate states that may be significantly different from the better-known modern climates which the models are formulated to reproduce. Simulating past climate states in AOGCMs and comparing the results to climate reconstructions based on proxies also allows us to test the models’ sensitivities to climate forcings and build confidence in their predictions of future climate.

[Emphasis added. And likewise for all bold text in future citations].

Their model:

For these simulations we use FAMOUS (FAst Met. Office and UK universities Simulator), a low resolution version of the Hadley Centre Coupled Model (HadCM3) AOGCM. FAMOUS has approximately half the spatial resolution of HadCM3, which reduces the computational cost of the model by a factor of 10.

[For more on the model, see note 1]

Their plan:

Here we present the first AOGCM transient simulations of the whole of the last glacial cycle. We have reduced the computational expense of these simulations by using FAMOUS, an AOGCM with a relatively low spatial resolution, and by accelerating the boundary conditions that we apply by a factor of ten, such that the 120,000 year cycle occurs in 12,000 years. We investigate how the influences of orbital variations in solar irradiance, GHGs and northern hemisphere ice-sheets combine to affect the evolution of the climate.

There is a problem with the speeding up process – the oceans respond on completely different timescales from the atmosphere. Some ocean processes take place over thousands of years, so whether or not the acceleration approach produces a real climate is open to discussion.

Their approach:

The aim of this study is to investigate the physical climate of the atmosphere and ocean through the last glacial cycle. Along with changes in solar insolation that result from variations in the Earth’s orbit around the sun, we treat northern hemisphere ice-sheets and changes in the GHG composition of the atmosphere as external forcing factors of the climate system which we specify as boundary conditions, either alone or in combination. Changes in solar activity, Antarctic ice, surface vegetation, or sea- level and meltwater fluxes implied by the evolving ice- sheets are not included in these simulations. Our experimental setup is thus somewhat simplified, with certain potential climate feedbacks excluded. Although partly a matter of necessity due to missing or poorly modelled processes in this version of FAMOUS, this simplification allows us to more clearly see the influence of the specified forcings, as well as ensuring that the simulations stay close to the real climate.

Let’s understand the key points of this modeling exercise:

A full GCM is used, but at reduced spatial resolution

The forcings are speeded up by a factor of 10 over their real life versions

Two of the critical forcings applied are actually feedbacks that need to be specified to make the model work – that is, the model is not able to calculate these critical feedbacks (CO2 concentration and ice sheet extent)

Five different simulations were run to see the effect of different factors:

Orbital forcing only applied (ORB)

GHG only forcing applied (GHG)

Ice sheet extent only applied (ICE)

All of the above with 2 different ice sheet reconstructions (ALL-ZH & ALL-5G – note that ALL-ZH has the same ice sheet reconstruction as ICE, while ALL-5G has a different one)

Here are the modeled temperature results compared against actual (Black) for Antarctica and Greenland:

From Smith & Gregory 2012

Figure 1

Lots of interesting things to note here.

When we look at Antarctica we see that orbital forcing alone and Northern hemisphere ice sheets alone do little or nothing to model past temperatures. But GHG concentrations by themselves as a forcing provide a modeled temperature that is broadly similar to the last 120kyrs – apart from higher frequency temperature variations, something we return to later. When we add the NH ice sheets we get an even better match. I’m surprised that the ice sheets don’t have more impact given that amount of solar radiation they reflect.

Both GHGs and ice sheets can be seen as positive feedbacks in reality (although in this model they are specified), and for the southern polar region GHGs have a much bigger effect.

Looking at Greenland, we see that orbital forcing once again has little effect on its own, while GHGs and ice sheets alone have similar effects but individually are a long way off the actual climate. Combining into all forcings, we see a reasonable match with actual temperatures with one sheet reconstruction and not so great a match for the other. This implies – for other models that try to model dynamic ice sheets (rather than specify) the accuracy may be critical for modeling success.

We again see that higher frequency temperature variations are not modeled at all well, and even some lower frequency variations – for example the period from 110 kyr to 85 kyr has some important missing variability (in the model).

The authors note:

The EPICA data [Antarctica] shows that, relative to their respective longer term trends, temperature fell more rapidly than CO2 during this period [120 – 110 kyrs], but in our experiments simulated Antarctic temperatures drop in line with CO2. This suggests that there is an important missing feedback in our model, or that our model is perhaps over-sensitive to CO2, and under-sensitive to one of the other forcing factors. Tests of the model where the forcings were not artificially accelerated rule out the possibility of the acceleration being a factor.

Abrupt Climate Change

What about the higher frequency temperature signals? The Greenland data has a much larger magnitude than Antarctica for this frequency, but neither are really reproduced in the model.

The other striking difference between the model and the NGRIP reconstruction is the model’s lack of the abrupt, millennial scale events of large amplitude in the ice-core data. It is thought that periodic surges of meltwater from the northern hemisphere ice-sheets and subsequent disruption of oceanic heat transports are involved in these events (Bond et al. 1993; Blunier et al. 1998), and the lack of ice-sheet meltwater runoff in our model is probably a large part of the reason why we do not simulate them.

The authors then discuss this a little more as the story is not at all settled and conclude:

Taken together, the lack of both millennial scale warm events in the south and abrupt events in the north strongly imply a missing feedback of some importance in our model.

CO2 Feedback

The processes by which sufficient quantities of carbon are drawn down into the glacial ocean to produce the atmospheric CO2 concentrations seen in ice-core records are not well understood, and have to date not been successfully modelled by a realistic coupled model. FAMOUS, as used in this study, does have a simple marine biogeochemistry model, although it does not respond to the forcings in these simulations in a way that would imply an increased uptake of carbon. A further FAMOUS simulation with interactive atmospheric CO2 did not produce any significant changes in atmospheric CO2 during the early glacial when forced with orbital variations and a growing northern hemisphere ice-sheet.

Accurately modelling a glacial cycle with interactive carbon chemistry requires a significant increase in our understanding of the processes involved, not simply the inclusion of a little extra complexity to the current model.

Conclusion

This is a very interesting paper, highlighting some successes, computational limitations, poorly understand feedbacks and missing feedbacks in climate models.

The fact that 120 kyrs of climate history has been simulated with a full GCM is great to see.

The lack of abrupt climate change in the simulation, the failure to track the fast rate of temperature fall at the start of ice age inception and the lack of ability to model key feedbacks all indicate that climate models – at least as far as the ice ages are concerned – are at a rudimentary stage.

(This doesn’t mean they aren’t hugely sophisticated, it just means climate is a little bit tricky).

Twelve – GCM V – Ice Age Termination – very recent work from He et al 2013, using a high resolution GCM (CCSM3) to analyze the end of the last ice age and the complex link between Antarctic and Greenland

Thirteen – Terminator II – looking at the date of Termination II, the end of the penultimate ice age – and implications for the cause of Termination II

Fourteen – Concepts & HD Data – getting a conceptual feel for the impacts of obliquity and precession, and some ice age datasets in high resolution

References

Notes

Note 1: FAMOUS

The ocean component is based on the rigid-lid Cox-Bryan model (Pacanowski et al. 1990), and is run at a resolution of 2.5° latitude by 3.75° longitude, with 20 vertical levels. The atmosphere is based on the primitive equations, with a resolution of 5° latitude by 7.5° longitude with 11 vertical levels (see Table 1).

Version XDBUA of FAMOUS (simply FAMOUS hereafter, see Smith et al. (2008) for full details) has a preindustrial control climate that is reasonably similar to that of HadCM3, although FAMOUS has a high latitude cold bias in the northern hemisphere during winter of about 5°C with respect to HadCM3 (averaged north of 40°N), and a consequent overestimate of winter sea-ice extent in the North Atlantic.

The global climate sensitivity of FAMOUS to increases in atmospheric CO2 is, however, similar to that of HadCM3.

FAMOUS incorporates a number of differences from HadCM3 intended to improve its climate simulation—for example, Iceland has been removed (Jones 2003) to encourage more northward ocean heat transport in the Atlantic. Smith and Gregory (2009) demonstrate that the sensitivity of the Atlantic meridional overturning circulation (AMOC) to perturbations in this version of FAMOUS is in the middle of the range when compared to many other coupled climate models. The model used in this study differs from XDBUA FAMOUS in that two technical bugs in the code have been fixed. Latent and sensible heat fluxes from the ocean were mistakenly interchanged in part of the coupling routine, and snow falling on sea-ice at coastal points was lost from the model. Correction of these errors results in an additional surface cold bias of a degree or so around high latitude coastal areas with respect to XDBUA, but no major changes to the model climatology. In addition, the basic land topography used in these runs was interpolated from the modern values in the ICE-5G dataset (Peltier 2004), which differs somewhat from the US Navy-derived topography used in Smith et al. (2008) and HadCM3.

In Part Seven we looked at a couple of papers from 1989 and 1994 which attempted to use GCMs to “start an ice age”. The evolution of the “climate science in progress” has been:

Finding indications that the timing of ice age inception was linked to redistribution of solar insolation via orbital changes – possibly reduced summer insolation in high latitudes (Hays et al 1976 – discussed in Part Three)

Using simple energy balance models to demonstrate there was some physics behind the plausible ideas (we saw a subset of the plausible ideas in Part Six – Hypotheses Abound)

Using a GCM with the starting conditions of around 115,000 years ago to see if “perennial snow cover” could be achieved at high latitudes that weren’t ice covered in the last inter-glacial – i.e., can we start a new ice age?

Why, if an energy balance model can “work”, i.e., produce perennial snow cover to start a new ice age, do we need to use a more complex model? As Rind and his colleagues said in their 1989 paper:

Various energy balance climate models have been used to assess how much cooling would be associated with changed orbital parameters.. With the proper tuning of parameters, some of which is justified on observational grounds, the models can be made to simulate the gross glacial/interglacial climate changes. However, these models do not calculate from first principles all the various influences on surface air temperature noted above, nor do they contain a hydrologic cycle which would allow snow cover to be generated or increase. The actual processes associated with allowing snow cover to remain through the summer will involve complex hydrologic and thermal influences, for which simple models can only provide gross approximations.

[Emphases added – and likewise in all following quotations, bold is emphasis added]. So interestingly, moving to a more complex model with better physics showed that there was a problem with (climate models) starting an ice age. Still, that was early GCMs with much more limited computing power. In this article we will look at the results a decade or so later.

Reviews

We’ll start with a couple of papers that include excellent reviews of “the problem so far”, one in 2002 by Yoshimori and his colleagues and one in 2004 by Vettoretti & Peltier. Yoshimori et al 2002:

One of the fundamental and challenging issues in paleoclimate modelling is the failure to capture the last glacial inception (Rind et al. 1989)..

..Between 118 and 110 kaBP, the sea level records show a rapid drop of 50 – 80 m from the last interglacial, which itself had a sea level only 3 – 5 m higher than today. This sea level lowering, as a reference, is about half of the last glacial maximum. ..As the last glacial inception offers one of few valuable test fields for the validation of climate models, particularly atmospheric general circulation models (AGCMs), many studies regarding this event have been conducted.

Phillipps & Held (1994) and Gallimore & Kutzbach (1995).. conducted a series of sensitivity experiments with respect to orbital parameters by specifying several extreme orbital configurations. These included a case with less obliquity and perihelion during the NH winter, which produces a cooler summer in the NH. Both studies came to a similar conclusion that although a cool summer orbital configuration brings the most favorable conditions for the development of permanent snow and expansion of glaciers, orbital forcing alone cannot account for the permanent snow cover in North America and Europe.

This conclusion was confirmed by Mitchell (1993), Schlesinger & Verbitsky (1996), and Vavrus (1999).. ..Schlesinger & Verbitsky (1996), integrating an ice sheet-asthenosphere model with AGCM output, found that a combination of orbital forcing and greenhouse forcing by reduced CO2 and CH4 was enough to nucleate ice sheets in Europe and North America. However, the simulated global ice volume was only 31% of the estimate derived from proxy records.

..By using a higher resolution model, Dong & Valdes (1995) simulated the growth of perennial snow under combined orbital and CO2 forcing. As well as the resolution of the model, an important difference between their model and others was the use of “envelope orography” [playing around with the height of land].. found that the changes in sea surface temperature due to orbital perturbations played a very important role in initiating the Laurentide and Fennoscandian ice sheets.

And as a note on the last quote, it’s important to understand that these studies were with an Atmospheric GCM, not an Atmospheric Ocean GCM – i.e., a model of the atmosphere with some prescribed sea surface temperatures (these might be from a separate run using a simpler model, or from values determined from proxies). The authors then comment on the potential impact of vegetation:

..The role of the biosphere in glacial inception has been studied by Gallimore & Kutzbach (1996), de Noblet et al. (1996), and Pollard and Thompson (1997).

..Gallimore & Kutzbach integrated an AGCM with a mixed layer ocean model under five different forcings: 1) control; 2) orbital; 3) #2 plus CO2; 4) #3 plus 25% expansion of tundra based on the study of Harrison et al. (1995); and (5) #4 plus further 25% expansion of tundra. The effect of the expansion of tundra through a vegetation-snow masking feedback was approximated by increasing the snow cover fraction. In only the last case was perennial snow cover seen..

..Pollard and Thompson (1997) also conducted an interactive vegetation and AGCM experiment under both orbital and CO2 forcing. They further integrated a dynamic ice-sheet model for 10 ka under the surface mass balance calculated from AGCM output using a multi-layer snow/ice-sheet surface column model on the grid of the dynamical ice-sheet model including the effect of refreezing of rain and meltwater. Although their model predicted the growth of an ice sheet over Baffin Island and the Canadian Archipelago, it also predicted a much faster growth rate in north western Canada and southern Alaska, and no nucleation was seen on Keewatin or Labrador [i.e. the wrong places]. Furthermore, the rate of increase of ice volume over North America was an order of magnitude less than that estimated from proxy records.

They conclude:

It is difficult to synthesise the results of these earlier studies since each model used different parameterisations of unresolved physical processes, resolution, and had different control climates as well as experimental design.

They summarize that results to date indicate that orbital forcing alone nor CO2 alone can explain glacial inception, and the combined effects are not consistent. And the difficulty appears to relate to the resolution of the model or feedback from the biosphere (vegetation).

A couple of years later Vettoretti & Peltier (2004) had a good review at the start of their paper.

Initial attempts to gain deeper understanding of the nature of the glacial–interglacial cycles involved studies based upon the use of simple energy balance models (EBMs), which have been directed towards the simulation of perennial snow cover under the influence of appropriately modified orbital forcing (e.g. Suarez and Held, 1979).

Analyses have since evolved such that the models of the climate system currently employed include explicit coupling of ice sheets to the EBM or to more complete AGCM models of the atmosphere.

The most recently developed models of the complete 100 kyr iceage cycle have evolved to the point where three model components have been interlinked, respectively, an EBM of the atmosphere that includes the influence of ice-albedo feedback including both land ice and sea ice, a model of global glaciology in which ice sheets are forced to grow and decay in response to meteorologically mediated changes in mass balance, and a model of glacial isostatic adjustment, through which process the surface elevation of the ice sheet may be depressed or elevated depending upon whether accumulation or ablation is dominant..

..Such models have also been employed to investigate the key role that variations in atmospheric carbon dioxide play in the 100 kyr cycle, especially in the transition out of the glacial state (Tarasov and Peltier, 1997; Shackleton, 2000). Since such models are rather efficient in terms of the computer resources required to integrate them, they are able to simulate the large number of glacial– interglacial cycles required to understand model sensitivities.

There has also been a movement within the modelling community towards the use of models that are currently referred to as earth models of intermediate complexity (EMICs) which incorporate sub-components that are of reduced levels of sophistication compared to the same components in modern Global ClimateModels (GCMs). These EMICs attempt to include representations of most of the components of the real Earth system including the atmosphere, the oceans, the cryosphere and the biosphere/carbon cycle (e.g. Claussen, 2002). Such models have provided, and will continue to provide, useful insight into long-term climate variability by making it possible to perform a large number of sensitivity studies designed to investigate the role of various feedback mechanisms that result from the interaction between the components that make up the climate system (e.g. Khodri et al., 2003).

Then the authors comment on the same studies and issues covered by Yoshimori et al, and additionally on their own 2003 paper and another study. On their own research:

Vettoretti and Peltier (2003a), more recently, have demonstrated that perennial snow cover is achieved in a recalibrated version of the CCCma AGCM2 solely as a consequence of orbital forcing when the atmospheric CO2 concentration is fixed to the pre-industrial level as constrained by measurements on air bubbles contained in the Vostok ice core (Petit et al., 1999).

This AGCM simulation demonstrated that perennial snow cover develops at high northern latitudes without the necessity of including any feedbacks due to vegetation or other effects. In this work, the process of glacial inception was analysed using three models having three different control climates that were, respectively, the original CCCma cold biased model, a reconfigured model modified so as to be unbiased, and a model that was warm biased with respect to the modern set of observed AMIP2 SSTs.. ..Vettoretti and Peltier (2003b) suggested a number of novel feedback mechanisms to be important for the enhancement of perennial snow cover.

In particular, this work demonstrated that successively colder climates increased moisture transport into glacial inception sensitive regions through increased baroclinic eddy activity at mid- to high latitudes. In order to assess this phenomenon quantitatively, a detailed investigation was conducted of changes in the moisture balance equation under 116 ka BP orbital forcing for the Arctic polar cap. As well as illustrating the action of a ‘‘cyrospheric moisture pump’’, the authors also proposed that the zonal asymmetry of the inception process at high latitudes, which has been inferred on the basis of geological observations, is a consequence of zonally heterogeneous increases and decreases of the northwards transport of heat and moisture.

And they go on to discuss other papers with an emphasis on moisture transport poleward. Now we’ll take a look at some work from that period.

Newer GCM work

Yoshimori et al 2002

Their models – an AGCM (atmospheric GCM) with 116kyrs orbital conditions and a) present day SSTs b) 116 kyrs SSTs. Then another model run with the above conditions and changed vegetation based on temperature (if the summer temperature is less than -5ºC the vegetation type is changed to tundra). Because running a “fully coupled” GCM (atmosphere and ocean) over a long time period required too much computing resources a compromise approach was used.

The SSTs were calculated using an intermediate complexity model, with a simple atmospheric model and a full ocean model (including sea ice) – and by running the model for 2000 years (oceans have a lot of thermal inertia). The details of this is described in section 2.1 of their paper. The idea is to get some SSTs that are consistent between ocean and atmosphere.

The SSTs are then used as boundary conditions for a “proper” atmospheric GCM run over 10 years – this is described in section 2.2 of their paper. The insolation anomaly, with respect to present day:

Figure 1

They use 240 ppm CO2 for the 116 kyr condition, as “the lowest probably equivalent CO2 level” (combining radiative forcing of CO2 and CH4). This equates to a reduction of 2.2 W/m² of radiative forcing. The SSTs calculated from the preliminary model are colder globally by 1.1ºC for the 116 kyr condition compared to the present day SST run. This is not due to the insolation anomaly, which just “redistributes” solar energy, it is due to the lower atmospheric CO2 concentration. The 116kyr SST in the northern North Atlantic is about 6ºC colder. This is due to the lower insolation value in summer plus a reduction in the MOC (note 1). The results of their work:

with modern SSTs, orbital and CO2 values from 116 kyrs – small extension of perennial snow cover

with calculated 116 kyr SST, orbital and CO2 values – a large extension in perennial snow cover into Northern Alaska, eastern Canada and some other areas

with vegetation changes (tundra) added – further extension of snow cover north of 60º

They comment (and provide graphs) that increased snow cover is partly from reduced snow melt but also from additional snowfall. This is the case even though colder temperatures generally favor less precipitation.

Contrary to the earlier ice age hypothesis, our results suggest that the capturing of glacial inception at 116kaBP requires the use of “cooler” sea surface conditions than those of the present climate. Also, the large impact of vegetation change on climate suggests that the inclusion of vegetation feedback is important for model validation, at least, in this particular period of Earth history.

What we don’t find out is why their model produces perennial snow cover (even without vegetation changes) where earlier attempts failed. What appears unstated is that although the “orbital hypothesis” is “supported” by the paper, the necessary conditions are colder sea surface temperatures induced by much lower atmospheric CO2. Without the lower CO2 this model cannot start an ice age. And an additional point to note, Vettoretti & Peltier 2004, say this about the above paper:

The meaningfulness of these results, however, remain to be seen as the original CCCma AGCM2 model is cold biased in summer surface temperature at high latitudes and sensitive to the low value of CO2 specified in the simulations.

Vettoretti & Peltier 2003

This is the paper referred to by their 2004 paper.

This simulation demonstrates that entry into glacial conditions at 116 kyr BP requires only the introduction of post-Eemian orbital insolation and standard preindustrial CO2 concentrations

Here are the seasonal and latitudinal variations in solar TOA of 116 kyrs ago vs today:

From Vettoretti & Peltier 2003

The essence of their model testing was they took an atmospheric GCM coupled to prescribed SSTs – for three different sets of SSTs – with orbital and GHG conditions from 116 kyrs BP and looked to see if perennial snow cover occurred (and where):

The three 116 kyr BP experiments demonstrated that glacial inception was successfully achieved in two of the three simulations performed with this model.

The warm-biased experiment delivered no perennial snow cover in the Arctic region except over central Greenland.

The cold-biased 116 kyr BP experiment had large portions of the Arctic north of 608N latitude covered in perennial snowfall. Strong regions of accumulation occurred over the Canadian Arctic archipelago and eastern and central Siberia. The accumulation over eastern Siberia appears to be excessive since there is little evidence that eastern Siberia ever entered into a glacial state. The accumulation pattern in this region is likely a result of the excessive precipitation in the modern simulation.

They also comment:

All three simulations are characterized by excessive summer precipitation over the majority of the polar land areas. Likewise, a plot of the annual mean precipitation in this region of the globe (not shown) indicates that the CCCma model is in general wet biased in the Arctic region. It has previously been demonstrated that the CCCma GCMII model also has a hydrological cycle that is more vigorous than is observed (Vettoretti et al. 2000b).

I’m not clear how much the model bias of excessive precipitation also affects their result of snow accumulation in the “right” areas.

In Part II of their paper they dig into the details of the changes in evaporation, precipitation and transport of moisture into the arctic region.

Crucifix & Loutre 2002

This paper (and the following paper) used an EMIC – an intermediate complexity model – which is a trade off model that has courser resolution, simpler parameterization but consequently much faster run time – allowing for lots of different simulations over much longer time periods than can be done with a GCM. The EMICs are also able to have coupled biosphere, ocean, ice sheets and atmosphere – whereas the GCM runs we saw above had only an atmospheric GCM with some method of prescribing sea surface temperatures.

This study addresses the mechanisms of climatic change in the northern high latitudes during the last interglacial (126–115 kyr BP) using the earth system model of intermediate complexity ‘‘MoBidiC’’.

Two series of sensitivity experiments have been performed to assess (a) the respective roles played by different feedbacks represented in the model and (b) the respective impacts of obliquity and precession..

..MoBidiC includes representations for atmosphere dynamics, ocean dynamics, sea ice and terrestrial vegetation. A total of ten transient experiments are presented here..

..The model simulates important environmental changes at northern high latitudes prior the last glacial inception, i.e.: (a) an annual mean cooling of 5 °C, mainly taking place between 122 and 120 kyr BP; (b) a southward shift of the northern treeline by 14° in latitude; (c) accumulation of perennial snow starting at about 122 kyr BP and (d) gradual appearance of perennial sea ice in the Arctic.

..The response of the boreal vegetation is a serious candidate to amplify significantly the orbital forcing and to trigger a glacial inception. The basic concept is that at a large scale, a snow field presents a much higher albedo over grass or tundra (about 0.8) than in forest (about 0.4).

..It must be noted that planetary albedo is also determined by the reflectance of the atmosphere and, in particular, cloud cover. However, clouds being prescribed in MoBidiC, surface albedo is definitely the main driver of planetary albedo changes.

In their summary:

At high latitudes, MoBidiC simulates an annual mean cooling of 5 °C over the continents and a decrease of 0.3 °C in SSTs.

This cooling is mainly related to a decrease in the shortwave balance at the top-of-the atmosphere by 18 W/m², partly compensated for by an increase by 15 W/m² in the atmospheric meridional heat transport divergence.

These changes are primarily induced by the astronomical forcing but are almost quadrupled by sea ice, snow and vegetation albedo feedbacks. The efficiency of these feedbacks is enhanced by the synergies that take place between them. The most critical synergy involves snow and vegetation and leads to settling of perennial snow north of 60°N starting 122 kyr BP. The temperature-albedo feedback is also responsible for an acceleration of the cooling trend between 122 and 120 kyr BP. This acceleration is only simulated north of 60° and is absent at lower latitudes.

See note 2 for details on the model. This model has a cold bias of up to 5°C in the winter high latitudes.

Calov et al 2005

We study the mechanisms of glacial inception by using the Earth system model of intermediate complexity, CLIMBER-2, which encompasses dynamic modules of the atmosphere, ocean, biosphere and ice sheets. Ice-sheet dynamics are described by the three- dimensional polythermal ice-sheet model SICOPOLIS. We have performed transient experiments starting at the Eemian interglacial, at 126 ky BP (126,000 years before present). The model runs for 26 kyr with time-dependent orbital and CO2 forcings.

The model simulates a rapid expansion of the area covered by inland ice in the Northern Hemisphere, predominantly over Northern America, starting at about 117 kyr BP. During the next 7 kyr, the ice volume grows gradually in the model at a rate which corresponds to a change in sea level of 10 m per millennium.

We have shown that the simulated glacial inception represents a bifurcation transition in the climate system from an interglacial to a glacial state caused by the strong snow-albedo feedback. This transition occurs when summer insolation at high latitudes of the Northern Hemisphere drops below a threshold value, which is only slightly lower than modern summer insolation.

By performing long-term equilibrium runs, we find that for the present-day orbital parameters at least two different equilibrium states of the climate system exist—the glacial and the interglacial; however, for the low summer insolation corresponding to 115 kyr BP we find only one, glacial, equilibrium state, while for the high summer insolation corresponding to 126 kyr BP only an interglacial state exists in the model.

We can get some sense of the simplification of the EMIC from the resolution:

The atmosphere, land- surface and terrestrial vegetation models employ the same grid with latitudinal resolution of 10° and longitudinal resolution of approximately 51°

Their ice sheet model has much more detail, with about 500 “cells” of the ice sheet fitting into 1 cell of the land surface model.

They also comment on the general problems (so far) with climate models trying to produce ice ages:

We speculate that the failure of some climate models to successfully simulate a glacial inception is due to their coarse spatial resolution or climate biases, that could shift their threshold values for the summer insolation, corresponding to the transition from interglacial to glacial climate state, beyond the realistic range of orbital parameters.

Another important factor determining the threshold value of the bifurcation transition is the albedo of snow.

In our model, a reduction of averaged snow albedo by only 10% prevents the rapid onset of glaciation on the Northern Hemisphere under any orbital configuration that occurred during the Quaternary. It is worth noting that the albedo of snow is parameterised in a rather crude way in many climate models, and might be underestimated. Moreover, as the albedo of snow strongly depends on temperature, the under-representation of high elevation areas in a coarse- scale climate model may additionally weaken the snow– albedo feedback.

Conclusion

So in this article we have reviewed a few papers from a decade or so ago that have turned the earlier problems (see Part Seven) into apparent (preliminary) successes.

We have seen two papers using models of “intermediate complexity” and coarse spatial resolution that simulated the beginnings of the last ice age. And we have seen two papers which used atmospheric GCMs linked to prescribed ocean conditions that simulated perennial snow cover in critical regions 116 kyrs ago.

Definitely some progress.

But remember the note that the early energy balance models had concluded that perennial snow cover could occur due to the reduction in high latitude summer insolation – support for the “Milankovitch” hypothesis. But then the much improved – but still rudimentary – models of Rind et al 1989 and Phillipps & Held 1994 found that with the better physics and better resolution they were unable to reproduce this case. And many later models likewise.

We’ve yet to review a fully coupled GCM (atmosphere and ocean) attempting to produce the start of an ice age. In the next article we will take a look at a number of very recent papers, including Jochum et al (2012):

So far, however, fully coupled, nonflux-corrected primitive equation general circulation models (GCMs) have failed to reproduce glacial inception, the cooling and increase in snow and ice cover that leads from the warm interglacials to the cold glacial periods..

..The GCMs failure to recreate glacial inception [see Otieno and Bromwich (2009) for a summary], which indicates a failure of either the GCMs or of Milankovitch’s hypothesis. Of course, if the hypothesis would be the culprit, one would have to wonder if climate is sufficiently understood to assemble a GCM in the first place.

We will also see that the strength of feedback mechanisms that contribute to perennial snow cover varies significantly for different papers.

And one of the biggest problems still being run into is the computing power necessary. From Jochum (2012) again:

This experimental setup is not optimal, of course. Ideally one would like to integrate the model from the last interglacial, approximately 126 kya ago, for 10,000 years into the glacial with slowly changing orbital forcing. However, this is not affordable; a 100-yr integration of CCSM on the NCAR supercomputers takes approximately 1 month and a substantial fraction of the climate group’s computing allocation.

Twelve – GCM V – Ice Age Termination – very recent work from He et al 2013, using a high resolution GCM (CCSM3) to analyze the end of the last ice age and the complex link between Antarctic and Greenland

Thirteen – Terminator II – looking at the date of Termination II, the end of the penultimate ice age – and implications for the cause of Termination II

Fourteen – Concepts & HD Data – getting a conceptual feel for the impacts of obliquity and precession, and some ice age datasets in high resolution

Notes

1. MOC = meridional overturning current. The MOC is the “Atlantic heat conveyor belt” where the cold salty water in the polar region of the Atlantic sinks rapidly and forms a circulation which pulls (warmer) surface equatorial waters towards the poles.

2. Some specifics on MoBidiC from the paper to give some idea of the compromises:

MoBidiC links a zonally averaged atmosphere to a sectorial representation of the surface, i.e. each zonal band (5° in latitude) is divided into different sectors representing the main continents (Eurasia–Africa and America) and oceans (Atlantic, Pacific and Indian). Each continental sector can be partly covered by snow and similarly, each oceanic sector can be partly covered by sea ice (with possibly a covering snow layer). The atmospheric component has been described by Galle ́e et al. (1991), with some improvements given in Crucifix et al. (2001). It is based on a zonally averaged quasi-geostrophic formalism with two layers in the vertical and 5° resolution in latitude. The radiative transfer is computed by dividing the atmosphere into up to 15 layers.

The ocean component is based on the sectorially averaged form of the multi-level, primitive equation ocean model of Bryan (1969). This model is extensively described in Hovine and Fichefet (1994) except for some minor modifications detailed in Crucifix et al. (2001). A simple thermodynamic–dynamic sea-ice component is coupled to the ocean model. It is based on the 0-layer thermodynamic model of Semtner (1976), with modifications introduced by Harvey (1988a, 1992). A one-dimensional meridional advection scheme is used with ice velocities prescribed as in Harvey (1988a). Finally, MoBidiC includes the dynamical vegetation model VE- CODE developed by Brovkin et al. (1997). It is based on a continuous bioclimatic classification which describes vegetation as a composition of simple plant functional types (trees and grass). Equilibrium tree and grass fractions are parameterised as a function of climate expressed as the GDD0 index and annual precipitation. The GDD0 (growing degree days above 0) index is defined as the cumulate sum of the continental temperature for all days during which the mean temperature, expressed in degrees, is positive.

MoBidiC’s simulation of the present-day climate has been discussed at length in (Crucifix et al. 2002). We recall its main features. The seasonal cycle of sea ice is reasonably reproduced with an Arctic sea-ice area ranging from 5 · 106 (summer) to 15 · 106 km2 (winter), which compares favourably with present-day observations (6.2 · 106 to 13.9 · 106 km2, respectively, Gloersen et al. 1992). Nevertheless, sea ice tends to persist too long in spring, and most of its melting occurs between June and August, which is faster than in the observations. In the Atlantic Ocean, North Atlantic Deep Water forms mainly between 45 and 60°N and is exported at a rate of 12.4 Sv to the Southern Ocean. This export rate is compatible with most estimates (e.g. Schmitz 1995). Furthermore, the main water masses of the ocean are well reproduced, with recirculation of Antarctic Bottom Water below the North Atlantic Deep Water and formation of Antarctic Intermediate Water. However no convection occurs in the Atlantic north of 60°N, contrary to the real world. As a consequence, continental high latitudes suffer of a cold bias, up to 5 °C in winter. Finally, the treeline is around about 65°N, which is roughly comparable to zonally averaged observations (e.g. MacDonald et al. 2000) but experiments made with this model to study the Holocene climate revealed its tendency to overestimate the amplitude of the treeline shift in response to the astronomical forcing (Crucifix et al. 2002).